



import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						)



class     UpperCAmelCase_       :







							def __init__(  self,     __a,     __a=14,     __a=7,     __a=True,     __a=True,     __a=True,     __a=True,     __a=True,     __a=99,     __a=32,     __a=5,     __a=4,     __a=37,     __a="gelu",     __a=0.1,     __a=0.1,     __a=512,     __a=16,     __a=2,     __a=0.02,     __a=3,     __a=4,     __a=None,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       parent
														_lowerCAmelCase	:				Optional[Any]					  =       batch_size
														_lowerCAmelCase	:				Union[str, Any]					  =       seq_length
														_lowerCAmelCase	:				Optional[int]					  =       is_training
														_lowerCAmelCase	:				Optional[int]					  =       use_token_type_ids
														_lowerCAmelCase	:				List[Any]					  =       use_input_mask
														_lowerCAmelCase	:				str					  =       use_labels
														_lowerCAmelCase	:				int					  =       use_mc_token_ids
														_lowerCAmelCase	:				Optional[Any]					  =       vocab_size
														_lowerCAmelCase	:				Union[str, Any]					  =       hidden_size
														_lowerCAmelCase	:				Dict					  =       num_hidden_layers
														_lowerCAmelCase	:				Optional[int]					  =       num_attention_heads
														_lowerCAmelCase	:				List[str]					  =       intermediate_size
														_lowerCAmelCase	:				Any					  =       hidden_act
														_lowerCAmelCase	:				str					  =       hidden_dropout_prob
														_lowerCAmelCase	:				str					  =       attention_probs_dropout_prob
														_lowerCAmelCase	:				int					  =       max_position_embeddings
														_lowerCAmelCase	:				List[str]					  =       type_vocab_size
														_lowerCAmelCase	:				Tuple					  =       type_sequence_label_size
														_lowerCAmelCase	:				Any					  =       initializer_range
														_lowerCAmelCase	:				List[str]					  =       num_labels
														_lowerCAmelCase	:				List[Any]					  =       num_choices
														_lowerCAmelCase	:				Dict					  =       scope
														_lowerCAmelCase	:				int					  =       self.vocab_size - 1







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				List[Any]					  =       ids_tensor([self.batch_size, self.seq_length],     self.vocab_size)

														_lowerCAmelCase	:				int					  =       None
														if self.use_input_mask:
																					_lowerCAmelCase	:				Tuple					  =       random_attention_mask([self.batch_size, self.seq_length])

														_lowerCAmelCase	:				List[Any]					  =       None
														if self.use_token_type_ids:
																					_lowerCAmelCase	:				Union[str, Any]					  =       ids_tensor([self.batch_size, self.seq_length],     self.type_vocab_size)

														_lowerCAmelCase	:				Tuple					  =       None
														if self.use_mc_token_ids:
																					_lowerCAmelCase	:				Tuple					  =       ids_tensor([self.batch_size, self.num_choices],     self.seq_length)

														_lowerCAmelCase	:				int					  =       None
														_lowerCAmelCase	:				str					  =       None
														_lowerCAmelCase	:				List[str]					  =       None
														if self.use_labels:
																					_lowerCAmelCase	:				Union[str, Any]					  =       ids_tensor([self.batch_size],     self.type_sequence_label_size)
																					_lowerCAmelCase	:				Any					  =       ids_tensor([self.batch_size, self.seq_length],     self.num_labels)
																					_lowerCAmelCase	:				Tuple					  =       ids_tensor([self.batch_size],     self.num_choices)

														_lowerCAmelCase	:				str					  =       self.get_config()

														_lowerCAmelCase	:				Any					  =       ids_tensor([self.num_hidden_layers, self.num_attention_heads],     2)

														return (
														    config,
														    input_ids,
														    input_mask,
														    head_mask,
														    token_type_ids,
														    mc_token_ids,
														    sequence_labels,
														    token_labels,
														    choice_labels,
														)







							def   snake_case__ (  self):

														'''simple docstring'''

														return CTRLConfig(
														    vocab_size=self.vocab_size,     n_embd=self.hidden_size,     n_layer=self.num_hidden_layers,     n_head=self.num_attention_heads,     n_positions=self.max_position_embeddings,     pad_token_id=self.pad_token_id,     )







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a,     *__a):

														'''simple docstring'''

														_lowerCAmelCase	:				List[Any]					  =       CTRLModel(config=a_)
														model.to(a_)
														model.eval()

														model(a_,     token_type_ids=a_,     head_mask=a_)
														model(a_,     token_type_ids=a_)
														_lowerCAmelCase	:				List[str]					  =       model(a_)
														self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, self.seq_length, self.hidden_size))
														self.parent.assertEqual(len(result.past_key_values),     config.n_layer)







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a,     *__a):

														'''simple docstring'''

														_lowerCAmelCase	:				Union[str, Any]					  =       CTRLLMHeadModel(a_)
														model.to(a_)
														model.eval()

														_lowerCAmelCase	:				List[str]					  =       model(a_,     token_type_ids=a_,     labels=a_)
														self.parent.assertEqual(result.loss.shape,     ())
														self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.vocab_size))







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Any					  =       self.prepare_config_and_inputs()

														(
														    _lowerCAmelCase		
														)	:				Dict					  =       config_and_inputs

														_lowerCAmelCase	:				List[str]					  =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

														return config, inputs_dict







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     *__a):

														'''simple docstring'''

														_lowerCAmelCase	:				str					  =       self.num_labels
														_lowerCAmelCase	:				List[str]					  =       CTRLForSequenceClassification(a_)
														model.to(a_)
														model.eval()
														_lowerCAmelCase	:				Union[str, Any]					  =       ids_tensor([self.batch_size],     self.type_sequence_label_size)
														_lowerCAmelCase	:				Dict					  =       model(a_,     token_type_ids=a_,     labels=a_)
														self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels))



@require_torch
class     UpperCAmelCase_       (			_A  ,   _A  ,   _A  ,   unittest.TestCase):
							lowerCamelCase__       =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowerCamelCase__       =						(CTRLLMHeadModel,) if is_torch_available() else ()
							lowerCamelCase__       =						(
							    {
							        'feature-extraction': CTRLModel,
							        'text-classification': CTRLForSequenceClassification,
							        'text-generation': CTRLLMHeadModel,
							        'zero-shot': CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase__       =						True
							lowerCamelCase__       =						False
							lowerCamelCase__       =						False







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a):

														'''simple docstring'''

														if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																					# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																					# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																					# config could not be created.
																					return True

														return False







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Tuple					  =       CTRLModelTester(self)
														_lowerCAmelCase	:				List[Any]					  =       ConfigTester(self,     config_class=a_,     n_embd=37)







							def   snake_case__ (  self):

														'''simple docstring'''

														super().tearDown()
														# clean-up as much as possible GPU memory occupied by PyTorch
														gc.collect()
														torch.cuda.empty_cache()







							def   snake_case__ (  self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				str					  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_ctrl_model(*a_)







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_lm_head_model(*a_)







							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
							def   snake_case__ (  self):

														'''simple docstring'''

														pass







							@slow
							def   snake_case__ (  self):

														'''simple docstring'''

														for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowerCAmelCase	:				List[str]					  =       CTRLModel.from_pretrained(a_)
																					self.assertIsNotNone(a_)







							@unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
							def   snake_case__ (  self):

														'''simple docstring'''

														pass



@require_torch
class     UpperCAmelCase_       (			unittest.TestCase):







							def   snake_case__ (  self):

														'''simple docstring'''

														super().tearDown()
														# clean-up as much as possible GPU memory occupied by PyTorch
														gc.collect()
														torch.cuda.empty_cache()







							@slow
							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[Any]					  =       CTRLLMHeadModel.from_pretrained("ctrl")
														model.to(a_)
														_lowerCAmelCase	:				List[Any]					  =       torch.tensor(
														    [[1_1859, 0, 1611, 8]],     dtype=torch.long,     device=a_)  # Legal the president is
														_lowerCAmelCase	:				Tuple					  =       [
														    1_1859,
														    0,
														    1611,
														    8,
														    5,
														    150,
														    2_6449,
														    2,
														    19,
														    348,
														    469,
														    3,
														    2595,
														    48,
														    2_0740,
														    24_6533,
														    24_6533,
														    19,
														    30,
														    5,
														]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

														_lowerCAmelCase	:				Tuple					  =       model.generate(a_,     do_sample=a_)
														self.assertListEqual(output_ids[0].tolist(),     a_)







'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



